Li Kuang’s research while affiliated with Central South University and other places

Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great challenges to SSL, resulting in performance degradation. Existing class-imbalanced semi-supervised learning (CISSL) methods mainly focus on rebalancing datasets but ignore the potential of using hard examples to enhance performance, making it difficult to fully harness the power of unlabeled data even with sophisticated algorithms. To address this issue, we propose a method that enhances the performance of Imbalanced Semi-Supervised Learning by Mining Hard Examples (SeMi). This method distinguishes the entropy differences among logits of hard and easy examples, thereby identifying hard examples and increasing the utility of unlabeled data, better addressing the imbalance problem in CISSL. In addition, we maintain a class-balanced memory bank with confidence decay for storing high-confidence embeddings to enhance the pseudo-labels' reliability. Although our method is simple, it is effective and seamlessly integrates with existing approaches. We perform comprehensive experiments on standard CISSL benchmarks and experimentally demonstrate that our proposed SeMi outperforms existing state-of-the-art methods on multiple benchmarks, especially in reversed scenarios, where our best result shows approximately a 54.8\% improvement over the baseline methods.

With the rapid advancement of flexible manufacturing in the Industrial Internet of Things (IIoT), there has been a significant increase in the number of IIoT devices and application software aimed at meeting various needs. The software defects may lead to delays or crashes in flexible manufacturing system, thereby affecting the production schedule. Automated software defect localization based on code changes can significantly reduce development and maintenance time costs, thereby maintaining the competitive edge of flexible manufacturing in the IIoT. Current efforts in software defect localization are primarily based on deep learning models or information retrieval models. This paper investigates the performance of Large Language Models (LLMs) in software defect localization and optimizes localization accuracy by combining it with an information retrieval model. Our empirical study reveals that GPT, given a software defect description, is unable to determine whether specific code changes are relevant. The model is unable to provide accurate answers, which aligns with the generative nature of LLMs where responses are generated according to probability distributions. However, the combined framework of LLMs and information retrieval models proposed in this paper outperforms the current state-of-the-art models on public datasets. We conclude that LLMs can enhance localization performance when used as side information in conjunction with existing information retrieval models. The effectiveness of the framework has been validated through experiments conducted on publicly available datasets and in practical applications within IIoT projects. This offers valuable insights into the application and development of LLMs for defect localization in the software development and maintenance processes in the IIoT flexible manufacturing.

Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and generalization. Notably, methods like TIGER employing Residual Quantization-based Semantic Identifiers (RQ-SID), have shown significant promise in e-commerce scenarios by effectively managing item IDs. However, a critical issue termed the "\textbf{Hourglass}" phenomenon, occurs in RQ-SID, where intermediate codebook tokens become overly concentrated, hindering the full utilization of generative retrieval methods. This paper analyses and addresses this problem by identifying data sparsity and long-tailed distribution as the primary causes. Through comprehensive experiments and detailed ablation studies, we analyze the impact of these factors on codebook utilization and data distribution. Our findings reveal that the "Hourglass" phenomenon substantially impacts the performance of RQ-SID in generative retrieval. We propose effective solutions to mitigate this issue, thereby significantly enhancing the effectiveness of generative retrieval in real-world E-commerce applications.